Error Rate Estimation Classification
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categories) and is analogous to the irreducible error.[1][2] A number of approaches to the estimation of the Bayes error rate exist. One method seeks to obtain analytical bounds oob estimate of error rate which are inherently dependent on distribution parameters, and hence difficult to estimate. Another
How To Calculate Classification Error Rate
approach focuses on class densities, while yet another method combines and compares various classifiers.[2] The Bayes error rate finds
Classification Error Rate Data Mining
important use in the study of patterns and machine learning techniques.[3] Error determination[edit] In terms of machine learning and pattern classification, the labels of a set of random observations can be divided
Classification Error Rate In R
into 2 or more classes. Each observation is called an instance and the class it belongs to is the label. The Bayes error rate of the data distribution is the probability an instance is misclassified by a classifier that knows the true class probabilities given the predictors. For a multiclass classifier, the Bayes error rate may be calculated as follows:[citation needed] p = ∫ minimum error rate classification x ∈ H i ∑ C i ≠ C max,x P ( C i | x ) p ( x ) d x {\displaystyle p=\textstyle \int \limits _{x\in H_{i}}\sum _{C_{i}\neq C_{\text{max,x}}}P(C_{i}|x)p(x)\,dx} where x is an instance, Ci is a class into which an instance is classified, Hi is the area/region that a classifier function h classifies as Ci.[clarification needed] The Bayes error is non-zero if the classification labels are not deterministic, i.e., there is a non-zero probability of a given instance belonging to more than one class.[citation needed] See also[edit] Naive Bayes classifier References[edit] ^ Fukunaga, Keinosuke (1990) Introduction to Statistical Pattern Recognition by ISBN 0122698517 pages 3 and 97 ^ a b K. Tumer, K. (1996) "Estimating the Bayes error rate through classifier combining" in Proceedings of the 13th International Conference on Pattern Recognition, Volume 2, 695–699 ^ Hastie, Trevor. The Elements of Statistical Learning (2nd ed.). http://statweb.stanford.edu/~tibs/ElemStatLearn/: Springer. p.17. ISBN978-0387848570. This statistics-related article is a stub. You can help Wikipedia by expanding it. v t e Retrieved from "https://en.wikipedia.org/w/index.php?title=Bayes_error_rate&oldid=743880528" Categories: Statistical classificationBayesian statisticsStatistics stubsHidden categories: All articles with unsourced statementsArticles with unsourced statements from February 2013Wikipedia articles needin
Republic of Korea Published in: ·Journal Computational Statistics & Data Analysis archive Volume 53 Issue 11, September, 2009 Pages 3735-3745 Elsevier Science Publishers B. V. Amsterdam, The Netherlands, The Netherlands tableofcontents doi>10.1016/j.csda.2009.04.009 classification error rate formula 2009 Article Bibliometrics ·Downloads (6 Weeks): n/a ·Downloads (12 Months): n/a bayes error rate example ·Downloads (cumulative): n/a ·Citation Count: 22 Recent authors with related interests Concepts in this article powered bayes error rate in r by Concepts inEstimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap Cross-validation (statistics) Cross-validation, sometimes called rotation estimation, is a technique for assessing how the results of a statistical https://en.wikipedia.org/wiki/Bayes_error_rate analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. morefromWikipedia Statistical classification In machine learning and statistics, classification is the problem of identifying which of a set of categories (sub-populations) a new observation belongs, on the basis http://dl.acm.org/citation.cfm?id=1551499 of a training set of data containing observations (or instances) whose category membership is known. The individual observations are analyzed into a set of quantifiable properties, known as various explanatory variables, features, etc. These properties may variously be categorical (e.g. morefromWikipedia Bias (statistics) A statistic is biased if it is calculated in such a way that is systematically different from the population parameter of interest. The following lists some types of, or aspects of, bias which should not be considered mutually exclusive: Selection bias, where individuals or groups are more likely to take part in a research project than others, resulting in biased samples. This can also be termed Berksonian bias. morefromWikipedia Estimator In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule and its result (the estimate) are distinguished. There are point and interval estimators. The point estimators yield single-valued results, although this includes the possibility of single vector-valued results and results that can be expressed as a single function. morefromWikipedia Tools and Resources TOC Service: Email
Login Help Contact Us About Access You are not currently logged in. Access your personal account or get JSTOR access through your library or other institution: login https://www.jstor.org/stable/24308531 Log in to your personal account or through your institution. If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader. Statistica Sinica Vol. 18, No. 3, July 2008 ON ERROR-RATE ESTIMA... ON error rate ERROR-RATE ESTIMATION IN NONPARAMETRIC CLASSIFICATION Anil K. Ghosh and Peter Hall Statistica Sinica Vol. 18, No. 3 (July 2008), pp. 1081-1100 Published by: Institute of Statistical Science, Academia Sinica Stable URL: http://www.jstor.org/stable/24308531 Page Count: 20 Read Online (Free) Subscribe ($19.50) Cite this Item Cite This Item Copy Citation Export Citation Export to RefWorks Export a RIS file (For EndNote, ProCite, Reference Manager, Zotero…) Export a Text classification error rate file (For BibTex) Note: Always review your references and make any necessary corrections before using. Pay attention to names, capitalization, and dates. × Close Overlay Journal Info Statistica Sinica Coverage: 1991-2014 (Vol. 1, No. 1 - Vol. 24, No. 4) Moving Wall Moving Wall: 1 year (What is the moving wall?) Moving Wall The "moving wall" represents the time period between the last issue available in JSTOR and the most recently published issue of a journal. Moving walls are generally represented in years. In rare instances, a publisher has elected to have a "zero" moving wall, so their current issues are available in JSTOR shortly after publication. Note: In calculating the moving wall, the current year is not counted. For example, if the current year is 2008 and a journal has a 5 year moving wall, articles from the year 2002 are available. Terms Related to the Moving Wall Fixed walls: Journals with no new volumes being added to the archive. Absorbed: Journals that are combined with another title. Complete: Journals that are no longer published or that have been combined with another title. ISSN: 10170405 EISSN: 19968507 Subjects: Mathematics, Science & Mathematics, Statistics ×